Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.
Artif Intell Gastroenterol. Nov 8, 2026; 7(3): 127488
Published online Nov 8, 2026. doi: 10.35712/aig.127488
Published online Nov 8, 2026. doi: 10.35712/aig.127488
Role of artificial intelligence in irritable bowel syndrome: A narrative review
Iyad A Issa, Faculty of Health Sciences, Lebanese University, Beirut 0000, Beyrouth, Lebanon
Taly Issa, University of Nicosia Medical School, Nicosia 24005, Cyprus
Author contributions: Issa I conceptualized and designed the review, performed the literature search, and drafted and critically revised the manuscript; Issa T contributed to the literature search, data interpretation, and drafting of the manuscript; and both authors have read and approved the final version of the manuscript.
AI contribution statement: During the preparation of this work, the authors used Claude AI for English-language polishing. The authors reviewed and edited the output as needed and take full responsibility for the content of the article.
Conflict-of-interest statement: The authors declare that they have no conflicts of interest.
Corresponding author: Iyad A Issa, MD, Faculty of Health Sciences, Lebanese University, Hadath, Beirut 0000, Beyrouth, Lebanon. iyadissa71@gmail.com
Received: September 2, 2026
Revised: September 18, 2026
Accepted: October 28, 2026
Published online: November 8, 2026
Processing time: 65 Days and 11.3 Hours
Revised: September 18, 2026
Accepted: October 28, 2026
Published online: November 8, 2026
Processing time: 65 Days and 11.3 Hours
Core Tip
Core Tip: Enthusiasm for artificial intelligence in irritable bowel syndrome has outpaced its evidence base. Across diagnosis, microbiota subtyping and treatment prediction, the supporting studies are small, single-center and largely externally unvalidated; a 2025 systematic review found only six eligible studies and could not pool them. Several widely repeated capabilities - machine-learning analysis of longitudinal symptom data, and of psychological comorbidity - have no peer-reviewed IBS evidence that we were able to identify. The field’s priority should therefore shift from developing further models toward standardized data collection, multi-center external validation, and reporting against artificial intelligence-specific standards.